Due to a lack of longitudinal, nationwide labor market data, evaluations of the impacts of energy communities remain unexplored. The authors propose a quasi-experimental approach using the Longitudinal Employer-Household Dynamics microdata from the U.S. Census Bureau to evaluate labor market outcomes in energy communities as defined by the Inflation Reduction Act. The authors will use the definition of an energy community to define an area “treated” by the law and then estimate labor market outcomes in these treated areas versus untreated areas. Based on this treatment definition, they will use a difference-in-differences approach to estimate the impact of the IRA tax credits to explore the heterogeneous impacts across worker demographic characteristics, geographic factors, and structural factors, such as local economies and energy mixes.

This project will use newly linked administrative microdata from the U.S. Census Bureau to provide the first comprehensive descriptive portrait of “energy communities,” evaluating whether the Inflation Reduction Act’s geographic targeting aligns with its stated equity goals. The authors will then examine historical patterns of firm entry and exit in these regions and track employment and earnings outcomes for affected workers. The analysis asks whether displaced fossil fuel workers or other local residents benefit when new firms enter—or whether these opportunities disproportionately go to in-migrants. These findings are poised to offer critical insights into the distributional impacts of place-based policy and establish an empirical foundation for evaluating the equity and effectiveness of clean energy investments.

This project will investigate how the Advanced Manufacturing Production Tax Credit (45X MPTC) and the Advanced Energy Project Investment Tax Credit (48C ITC) affect manufacturing activity throughout the solar photovoltaic supply chain, with a focus on its impacts in the United States. The central research questions are: How do the Inflation Reduction Act’s domestic manufacturing and content-based tax credits affect investment, production, and employment across different stages of the solar manufacturing supply chain? How do these effects propagate downstream to influence solar technology adoption and environmental outcomes? The author will do a reduced form analysis of manufacturing investment and output, develop a structural model of the global solar photovoltaic supply chain, and conduct simulations using the structural model. By focusing on the entire supply chain, from polysilicon and wafers to cells and modules, this research aims to provide the first comprehensive assessment of the Inflation Reduction Act’s economic and environmental implications in the solar industry.

This study seeks to compare the Inflation Reduction Act and tariff approaches by quantifying how each policy affects the new vehicle market and domestic manufacturing activity. The authors will evaluate how these policies affect purchase decisions and welfare of different consumer groups (by income, geography, and other demographics), as well as the production and profits of different manufacturer groups (based in the United States versus abroad) and the incentives each policy creates to expand U.S. industrial capacity. The authors will use an existing model of the electric vehicle market to examine the impact of switching from IRA subsidies to tariffs on EV prices, the impact of switching from IRA subsidies to tariffs on short-run domestic production and profits, and the impact of IRA subsidies on long-run investments in capacity and production.

Currently, no government surveys collect worker-level data on generative AI adoption and use. This proposal seeks to continue and enhance the Real-Time Population Survey, the first nationally representative survey tracking genAI adoption in U.S. workplaces. The Real-Time Population Survey, first launched in April 2020, is benchmarked to the Current Population Survey, allowing researchers to validate outcomes against the larger sample size. It builds upon previous technology adoption surveys, enabling comparisons to other information and communication technologies. Given the rapid pace of AI adoption thus far, adoption rates are expected to change substantially in the coming years. Funding would cover three additional surveys in the August 2025–July 2027 grant period and support the development of innovative questions on how genAI interacts with work tasks. The resulting data will provide insights into which workers use genAI, how often, and which tasks it complements or automates, helping to discipline and test theories of the labor market impact of genAI. Findings will also inform workforce development and social insurance policies with the goal of maximizing aggregate productivity gains while simultaneously ensuring that benefits are broadly distributed. This grant was co-funded by the Russell Sage Foundation.

This study will examine the emerging role of artificial intelligence in ongoing "modernization" initiatives at the California Department of Motor Vehicles and the impacts these changes have had on the agency's workforce. Public-sector employment has long provided a dependable pathway to the middle class for workers otherwise less likely to attain such job security, wages, and benefits based on their race, gender, geography, or educational attainment. The rapid ascendance of public-sector AI initiatives in California raises significant questions about the future of this longstanding opportunity for middle-class mobility. Through mixed methods analysis of public and private datasets, the team will assess the demographic and economic outcomes associated with specific AI technologies in use at the Department of Motor Vehicles. This will provide policymakers and labor advocates with a clearer sense of how to meaningfully intervene to bolster worker protections and sustain a diverse middle class amid widespread technological uncertainty.